Take-home Exercise 3

The focus of this exercise is to perform insightful data visualization to uncover the salient patterns of the resale prices of public housing property by residential towns and estates in Singapore.

Author

Affiliation

Liang Minghao

SMU, MITB

1. Overview

This take-home exercise aims to reveal the salient patterns of the resale prices of public housing properties by residential towns and estates in Singapore.

The dataset used in take-home exercise 3 is downloaded from Data.gov.sg, and processed by RStudio tidyverse family of packages and visualized by ggplot2 and its extensions.

1.1. Loading Libraries

Using pacman package to load visualization packages.

pacman::p_load(dplyr, tidyverse, DT, ggplot2, ggiraph, 
               ggstatsplot, patchwork, plotly, gganimate, 
               ggthemes, corrplot, heatmaply)

1.2. Loading Data

Previewing the head rows of the data to observe the columns.

resale_data <- read_csv("../Data/resale-flat-prices-based-on-registration-date-from-jan-2017-onwards.csv")

options(DT.options = list(pageLength = 5))
DT::datatable(head(resale_data,20),class ="cell-border stripe",style = "bootstrap") 

#%>% formatStyle(names(resale_data),backgroundColor = 'grey')

1.3. Data Pre-Processing

For this study, we are interested in the 3-ROOM, 4-ROOM and 5-ROOM flats that are sold during the year of 2022 (except using data of 2017-2023 for trend analysis).

unique(resale_data$flat_type)

[1] "2 ROOM"           "3 ROOM"           "4 ROOM"           "5 ROOM"          
[5] "EXECUTIVE"        "1 ROOM"           "MULTI-GENERATION"

First, we transform the data by extracting year and month from column month, and calculating the remaining lease years and price per square meter, using the tidyverse library.

resale_345 <- resale_data %>% 
  # and 3/4/5 ROOM in flat_type column
  filter(str_detect(flat_type, '3 ROOM|4 ROOM|5 ROOM')) %>%
  # extract the year of sale
  mutate(sale_year=as.integer(str_sub(month, start = 1L, end = 4L))) %>%
  # extract the month of sale
  mutate(sale_month=as.integer(str_sub(month, start = 6L, end = -1L))) %>%
  # extract the remaining lease in years
  mutate(remaining_lease_years=as.integer(str_sub(remaining_lease, start = 1L, end = 2L))) %>%
  # calculate the price/sqm
  mutate(psqm=round(resale_price / floor_area_sqm, 2))

DT::datatable(head(resale_345,100),class ="cell-border stripe",style = "bootstrap") %>%
DT::formatStyle(columns = colnames(.), fontSize = '20%')

2. Data Analysis and Visualizations

2.1. Timely Trend (From 2017 - 2023)

2.1.1. Timely Trend of Price Per Sqm. by Flat Type

grp2 <- resale_345 %>%
  group_by(sale_year, flat_type) %>%
  summarise(no_sales=n(), avg_psqm=round(mean(psqm),2))


tt <- c(paste("Year:", grp2$sale_year, "<br>Price/SQM: $", grp2$avg_psqm))

fig2 <-grp2 %>%
  ggplot(aes(x = sale_year, y = avg_psqm,colour = flat_type)) +
  geom_smooth(alpha = 0.1,se=FALSE) +
  geom_point_interactive(aes(tooltip = tt),size = 5) +
  theme_excel_new()+
  scale_x_continuous(breaks = seq(2017,2023,by = 1),limits = c(2017,2023)) +
  scale_y_continuous(breaks = seq(0,6500,by = 1000),limits = c(4000,6500)) +
  labs(title = "Avg. Resale Unit Price, 2017 - 2023", x = 'Year', y = 'Avg. Resale Price') +
  scale_color_manual(values = c("#3d7dcc", "#f57ee0", "#02e302")) 

girafe(
  ggobj = fig2, width_svg = 12
)

Consideration: lineplot is chosen for clearer observation of the change in average unit price. For more obvious illustration of the trend, the data was grouped by year instead of month. When user hover on the data points, the corresponding unit price of the specific flat type in that year will be displayed.

Analytical insights: from the visualization, we found out:

• Before 2020, the unit resale price in Singapore is fairly stable, fluctuating around SGD 4,500.
• After 2020, the average unit price increased rapidly year by year; this may be affected by the short in labour force due to COVID-19 and the increased demand.

2.1.2. Timely Trend of No. of Sales

# Group by month and flt type
grp <- resale_345 %>%
  group_by(month, flat_type) %>%
  summarise(no_sales=n(), avg_psqm=round(mean(psqm),2))

# Minimal point of No. of sales
m <- grp[which.min(grp$no_sales), ]
a <- list(
  x = m$month,
  y = m$no_sales,
  text = 'Circuit Breaker Period',
  xref = "x",
  yref = "y",
  showarrow = TRUE,
  arrowhead = 7,
  ax = -100,
  ay = -30
)

# Use Plotly to draw line chart by flat type
fig <- grp %>%
  ungroup() %>%
  plot_ly(x = ~month, 
          y = ~no_sales, 
          color= ~flat_type,
          text = ~paste("month:", month, "<br>Sales:", no_sales),
          type = 'scatter', 
          mode = 'lines') %>%
  layout(title = list(text = 'Total No. of Sales of 3/4/5 Room Flats, 2017 - 2022',
                      pad = list(b = 90, l = 130, r = 50 )
                      ),
         xaxis = list(title = 'MONTH',
                      zeroline = TRUE),
         yaxis = list(title = 'NO. OF SALES')
         )
# Add min point annotation
fig <- fig %>% add_markers()
fig <- fig %>% layout(annotations = a)

fig

Consideration: lineplot is chosen for clearer observation of the sales volume fluctuation. Different room types are represented by different colors of lines in the chart. As for interactability, when user hover on a particular data point, year and month and the respective sales volume will be displayed.

Analytical insights: from the visualization, we found out:

• 4-ROOM flats are the most popular flat type across the whole period, whereas the sales volume of 5-ROOM flats has been slightly higher than that of 3-ROOM flats most of the time.
• There is a noticable drop in sales volume during April & May 2020; this was probably due to the circuit breaker policy during COVID-19 spread in Singapore.

2.1.3. Timely Trend of Price Per Sqm. by Area

means=resale_345 %>% group_by(town) %>% summarise(mpsqm=mean(psqm))
means

# A tibble: 26 × 2
   town          mpsqm
   <chr>         <dbl>
 1 ANG MO KIO    4997.
 2 BEDOK         4823.
 3 BISHAN        6070.
 4 BUKIT BATOK   4400.
 5 BUKIT MERAH   6676.
 6 BUKIT PANJANG 4491.
 7 BUKIT TIMAH   6450.
 8 CENTRAL AREA  7850.
 9 CHOA CHU KANG 4158.
10 CLEMENTI      5885.
# … with 16 more rows

# Calculate the mean psqm for each town and year combination
means <- resale_345 %>% 
  group_by(town, sale_year) %>% 
  summarise(mpsqm = mean(psqm))

p <- ggplot(resale_345, aes(x = psqm, color = factor(town), fill = factor(town))) + 
  geom_density(aes(frame=resale_345$sale_year)) +
  facet_grid(town~., scales = "free", space = "free") +
  geom_vline(data = means, aes(xintercept = mpsqm, frame=sale_year), alpha=0.4) +
  theme_bw() +
  theme(
    legend.position = "none",
    plot.margin = margin(1, 3, 0, 0, "cm"),
    panel.grid = element_blank(),
    panel.background = element_blank(),
    axis.text = element_blank(),
    strip.text.y = element_text(angle = 0,margin = margin(2,2,2,2, "cm")),
    panel.spacing = unit(0, "cm", data = NULL)
  )+xlab('Price Per Sqm. (SGD)')

fig3 <- ggplotly(p) %>% layout(title="Price Per Sqm. Distribution by Town") 

fig3 <- fig3 %>% 
  animation_opts(
  1200, easing = "linear", redraw = FALSE,
  ) %>%
  animation_slider(
    currentvalue = list(prefix = "YEAR ", font = list(color="black"))
  )

fig3

Consideration: density plot of unit price distribution is chosen for comparision between different towns, and a vertical line indicates the median of the unit price in that area. User can also drag the slider bar mannually or press the “Play” button to see how the unit prices in different areas change over the years.

Analytical insights: here are the findings from the visualization:

• We can observe that towns such as Bukit Timah, Central Area and Queenstown have more left-skewed distributions, indicating these are more expensive areas;
• Areas like Woodlands, Yishun and Choa Chu Kang are more affordable ones.
• Moreover, using plotly, we can see how the medians “run” to the right-hand side since 2020 onwards!

2.2. Statistical Visualization (In 2022)

2.2.1. One-way ANOVA Test on Median of Price Per Sqm. (Non-parametric)

#filter only 2022 data
resale_2022 <- resale_345 %>%filter(sale_year==2022)

anova <- ggbetweenstats(
  data = resale_2022,
  x = flat_type,
  y = psqm,
  type = "np",                   #non-parametric test
  mean.ci = TRUE,
  pairwise.comparisons = TRUE,
  pairwise.display = "s",
  p.adjust.method = "hom"
) + xlab('Flat Type') + ylab('Price Per Sqm. (SGD)')

anova

Consideration: violin plots are used to show the unit price distribution of different flat type; by the p-values on top of the graphs, we can see which pair of samples have a rejected null hypothesis in the ANOVA test.

Analytical insights: here are the findings from the visualization:

• we can conclude that 5-ROOM flats has a statistically different median in unit price compare to 3 ROOM and 4 ROOM flats;

• we cannot conclude any statistical difference between median in unit price of 3-ROOM and 4-ROOM flats.

2.2.2. Boxplots of Resale Price by Flat Model

pp <- ggplot(resale_2022, aes(x=reorder(flat_model,resale_price,fun=median), y=resale_price, fill=flat_model))+
  geom_boxplot()+
  xlab('Flat Model Type')+
  ylab('Price in SGD')+
  theme(
    legend.position = "none")
fig4 <- ggplotly(pp) %>% 
  layout(
    title="By Flat Model",
    xaxis=list(tickangle = 45)
         )
fig4

Consideration: box plots are used to show the total resale price distribution of different flat model. The models are sorted by their median so that it is easier to observe which ones are more premium flat models.

Analytical insights: here are the findings from the visualization:

• Type S1, S2 and Premium Apartment Loft are generally more expensive;

• New Generation, Simplified and A2 Model are more affordable.

2.3. Ternary Diagram: Percentage of “Affordable”, “Medium”, “Premium” Flats by Area

resale_2022$tri_tile <- ntile(resale_2022$psqm, 3)
resale_2022_perc_by_town <- resale_2022 %>%
  group_by(town, tri_tile) %>%
  summarise(No_sales=n()) %>%
  mutate(freq = round(No_sales / sum(No_sales),2)) %>%
  select(town, tri_tile, freq) %>%
  pivot_wider(names_from = tri_tile, values_from = freq)
colnames(resale_2022_perc_by_town) <- c("town" ,"Affordable", "Medium","Premium")
resale_2022_perc_by_town <- resale_2022_perc_by_town %>% mutate(Affordable=replace_na(Affordable,0))
resale_2022_perc_by_town

# A tibble: 26 × 4
# Groups:   town [26]
   town          Affordable Medium Premium
   <chr>              <dbl>  <dbl>   <dbl>
 1 ANG MO KIO          0.28   0.45    0.27
 2 BEDOK               0.37   0.45    0.18
 3 BISHAN              0.06   0.1     0.85
 4 BUKIT BATOK         0.42   0.32    0.26
 5 BUKIT MERAH         0.07   0.25    0.69
 6 BUKIT PANJANG       0.47   0.37    0.15
 7 BUKIT TIMAH         0      0.12    0.88
 8 CENTRAL AREA        0      0.07    0.93
 9 CHOA CHU KANG       0.54   0.36    0.11
10 CLEMENTI            0.16   0.35    0.49
# … with 16 more rows

label <- function(txt) {
  list(
    text = txt, 
    x = 0.1, y = 1,
    ax = 0, ay = 0,
    xref = "paper", yref = "paper", 
    align = "center",
    font = list(family = "serif", size = 15, color = "black"),
    bordercolor = "black", borderwidth = 2
  )
}

axis <- function(txt) {
  list(
    title = txt, tickformat = ".0%", tickfont = list(size = 10)
  )
}

ternaryAxes <- list(
  aaxis = axis("Affordable"), 
  baxis = axis("Medium"), 
  caxis = axis("Premium")
)

plot_ly(
  resale_2022_perc_by_town,
  # a b c are the 3 variables, text is the tooltip (town)
  a = ~Affordable, 
  b = ~Medium, 
  c = ~Premium, 
  text = ~town,
  color = I('#DB7365'), 
  type = "scatterternary",
  marker = list(size = 8)
) %>%
  layout(
    annotations = label("Ternary Diagram: Distribution of Unit <br> in Different Price Range by Town"), 
    ternary = ternaryAxes,
    margin = margin(.5,.5,.5,.5, "cm")
  )

Consideration: We divide all HDB flats in to 3-tile based on their resale price: “Affordable”, “Medium” and “Premium”, and we would like to see how each of the portion constitute to the total number of units in diferent towns. When the a data point is hovered upon, the town, and percentage of units in each price range will be displayed.

Analytical insights: here are the findings from the visualization:

• We can found the data points tend to cluster around the “Affordable” angle and the “Premium” angle, which means that few towns are dominated by flats in medium price range.

• Close to the “Premium” angle are areas that are located near the downtown or central;

• Close to the “Affordable” angle are areas that are closer to the suburb.

2.4. Correlation Matrix: Factors that Affect Price

resale_cor <- cor(resale_2022[, c('resale_price', 'psqm', 'floor_area_sqm', 'remaining_lease_years')])
resale_sig <-  cor.mtest(resale_cor, conf.level= .95)
corrplot(resale_cor,
         method = "number",
         type = "lower",
         diag = TRUE,
         tl.col = "black",
         tl.srt = 45,
         p.mat = resale_sig$p,
         sig.level = .05)

Consideration: We would like to compute the correlation between each of the numerical variable to discover how much they are correlated to each other.

Analytical insights: By plotting the correlation matrix between resale_price, floor_area_sqm, remaining_lease_years and psqm, we find that the correlations between unit price of resale flat and total price, floor area and remaining lease period are not statistically significant.

2.5. Heatmap: Between Storey and Price

resale_storey <- resale_2022 %>%
  group_by(town, storey_range) %>%
  summarise(med_price=median(resale_price)) %>%
  ungroup() %>%
  mutate(town = reorder(town, med_price))

# Specify the number of colors in the color ramp
custom_colors <- colorRampPalette(c("blue","red"))(n = 20)

hmp <- plot_ly(
  data = resale_storey, 
  x = ~town ,
  y = ~storey_range, 
  z = ~med_price, 
  type = "heatmap",
  colorscale = list(
    reverse = TRUE, 
    cmin = min(resale_storey$med_price), 
    cmax = max(resale_storey$med_price), 
    colors = custom_colors)
) %>% layout(
  xaxis = list(title = "Town"),
  yaxis = list(title = "Storey Range"),
  title = "Resale Prices by Town and Storey Range"
)

hmp

Consideration: Heatmap is used to display the median price in each of the storey range for each town. A blue-read color palette is used to distingush the more expensive ones that the rest.

Analytical insights:

• For the areas that are closer to the central, it is an obvious trend that the median price increases as the storey range goes higher.

• Interestingly, this does not apply to the suburban area, as the change in color is not obvious with the increase in level.

3. Conclusions and Takeaways

Conclusions:

• From the visualizations, we are able to observe how the real estate market changes in the past several years, including sales volume and price, due to pandemic and its corresponding policy change.

• Furthermore, we can conclude that some of the most important factor that affect the resale price are location and flat model. For storey of flats, the “higher floor, higher price” relationship only applies to the heartland areas in the City, and is less obvious in the suburban areas.

• There are few areas that have majority of medium-priced (33%-66% percentile in resale price) flats in the city; most of the areas are dominated by either affordable or premium flats.

• Factors such as remaining lease years and floor area does not siginificantly affect the unit price of the flat.

Some of my key takeaways:

• There is no one visualization method that is always better than others. We need to adjust the type of visualization based on the characteristics of the data, the message to be conveyed, and even the target audience.

• “Occam’s razor” applies. Compared to graphs that are filled with a lot of information, we only need to display the important ones for clearer illustration of the insights.